The Ends, Not The Means
In an unexpected outcome, scientists were able to train rats to create and maintain a large difference in temperature between their two ears. The training mechanism was fairly simple — the larger the temperature delta, the larger the reward in the form of electrical stimulation. After a few weeks of training, the rodents were able to induce and maintain a temperature difference of 5°C between their left and right ears.
Identifying the specific pathway in a rat to increase one ear’s temperature rather than the other, and then to act on this pathway precisely enough to achieve the intended output seems like an unreasonably difficult task. The somewhat surprising outcome of the experiment described above points toward the opportunity to guide biological systems at a high-level toward a desired output, rather than attempting to control each step of a complex process at a fine-grain.
The idea that organisms, organs, tissues, and even cells can generate a desired outcome on their own if you create the proper incentives is a powerful one. It has led to important basic discoveries and could possibly be used in the near future to solve complex problems. Three variations of this idea serve as landmarks toward engineering biology in news ways:
1. Directed evolution is a method used in protein engineering that mimics the process of natural selection to steer proteins or nucleic acids toward a defined goal. This method appeared in the late 1960s and started with RNA. It quickly was used to evolve various proteins, and in particular enzymes able to catalyze challenging reactions. In 2018, Frances Arnold was awarded the Nobel Prize in Chemistry for her work on protein optimization via directed evolution. Directed evolution works on the principle that, when it comes to biological discovery, the path to optimization does not matter and should be left to the organism to work out. All that matters, and what engineers need to be focused on, is how best to set up biological systems to get them to find the preferred outcome.
2. Slime mold is quite a remarkable unicellular organism. A seminal paper from 2010 published in Science showed that the slime mold Physarum polycephalum is able to recreate complex networks with comparable efficiency, fault tolerance, and cost to those of real-world infrastructure systems such as the Tokyo rail system. The beauty of this work is that the mechanisms by which the unicellular organism creates the network can be captured in a biologically-inspired mathematical model—we have written about natural computing before and this is a prime example of it. Opening the black box on biological processes at the level of solutions produced (in this case adaptive network formation) should allow scientists and engineers to adapt these approaches to other domains.
3. Michael Levin’s work (featured in a recent issue of the The New Yorker if you want an expansive portrait of the man and his research) focuses on non-neurologic bioelectric signaling and identifies it as a key organizing principle in complex biological processes such as morphogenesis. One of the most striking examples showcasing the importance of bioelectric signaling is the distinction between growing a head or a tail in the planarian model organism. Solely by manipulating bioelectric circuits, Levin can control the regrowth of severed body parts while guiding the organism’s overall fate to outputs such as creating a two-headed animal. His novel strategy is to control morphogenesis not at the individual cellular level, but rather to trigger a master mechanism that governs the plan for the organism to make a head. Having done this, the organism retains the function of creating two-headed offspring, despite the fact that no changes have been made to its genome. This very unexpected outcome points toward the opportunity to guide biology to desired outcomes such as limb regeneration at the level of tissues, rather than by re-wiring systems from the molecular level on up.
These examples inspire us to think broadly about how to better guide biologic systems to take advantage of their already existing capabilities in engineering therapeutic outcomes.